Results 1  10
of
21,650
Enumerative lattice algorithms in any norm via mellipsoid coverings
 in FOCS
, 2011
"... We give a novel algorithm for enumerating lattice points in any convex body, and give applications to several classic lattice problems, including the Shortest and Closest Vector Problems (SVP and CVP, respectively) and Integer Programming (IP). Our enumeration technique relies on a classical concept ..."
Abstract

Cited by 18 (13 self)
 Add to MetaCart
to the lattice, on ndimensional lattices in any (semi)norm given an Mellipsoid of the unit ball. In many norms of interest, including all ℓp norms, an Mellipsoid is computable in deterministic poly(n) time, in which case these algorithms are fully deterministic. Here our approach may be seen as a
Enumerative Algorithms for the Shortest and Closest Lattice Vector Problems in Any Norm via MEllipsoid Coverings
, 2010
"... We give an algorithm for solving the exact Shortest Vector Problem in ndimensional lattices, in any norm, in deterministic 2 O(n) time (and space), given poly(n)sized advice that depends only on the norm. In many norms of interest, including all ℓp norms, the advice is efficiently and deterministi ..."
Abstract

Cited by 5 (1 self)
 Add to MetaCart
is to reduce the enumeration of lattice points in an arbitrary convex body K to enumeration in 2 O(n) copies of an Mellipsoid of K, a classical concept in asymptotic convex geometry. Building on the techniques of Klartag (Geometric and Functional Analysis, 2006), we also give an expected 2 O(n)time algorithm
Deterministic Construction of an Approximate MEllipsoid and its Applications to Derandomizing Lattice Algorithms
"... We give a deterministic O(log n) ntime and space algorithm for the Shortest Vector Problem (SVP) of a lattice under any norm, improving on the previous best deterministic nO(n)time algorithms for general norms. This approaches the 2O(n)time and space complexity of the randomized sieve based SVP a ..."
Abstract

Cited by 3 (2 self)
 Add to MetaCart
algorithms (Arvind and Joglekar, FSTTCS 2008), first introduced by Ajtai, Kumar and Sivakumar (STOC 2001) for ℓ2SVP, and the Mellipsoid covering based SVP algorithm of Dadush et al. (FOCS 2011). Here we continue with the covering approach of Dadush et al., and our main technical contribution is a
Planning Algorithms
, 2004
"... This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning ..."
Abstract

Cited by 1108 (51 self)
 Add to MetaCart
This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning
Improved Approximation Algorithms for Maximum Cut and Satisfiability Problems Using Semidefinite Programming
 Journal of the ACM
, 1995
"... We present randomized approximation algorithms for the maximum cut (MAX CUT) and maximum 2satisfiability (MAX 2SAT) problems that always deliver solutions of expected value at least .87856 times the optimal value. These algorithms use a simple and elegant technique that randomly rounds the solution ..."
Abstract

Cited by 1231 (13 self)
 Add to MetaCart
We present randomized approximation algorithms for the maximum cut (MAX CUT) and maximum 2satisfiability (MAX 2SAT) problems that always deliver solutions of expected value at least .87856 times the optimal value. These algorithms use a simple and elegant technique that randomly rounds
Nearoptimal deterministic algorithms for volume computation and lattice problems via mellipsoids
, 2012
"... We give a deterministic 2 O(n) algorithm for computing an Mellipsoid of a convex body, matching a known lower bound. This has several interesting consequences including improved deterministic algorithms for volume estimation of convex bodies and for the shortest and closest lattice vector problems ..."
Abstract

Cited by 3 (3 self)
 Add to MetaCart
We give a deterministic 2 O(n) algorithm for computing an Mellipsoid of a convex body, matching a known lower bound. This has several interesting consequences including improved deterministic algorithms for volume estimation of convex bodies and for the shortest and closest lattice vector problems
Consensus and cooperation in networked multiagent systems
 PROCEEDINGS OF THE IEEE
"... This paper provides a theoretical framework for analysis of consensus algorithms for multiagent networked systems with an emphasis on the role of directed information flow, robustness to changes in network topology due to link/node failures, timedelays, and performance guarantees. An overview of ..."
Abstract

Cited by 772 (2 self)
 Add to MetaCart
This paper provides a theoretical framework for analysis of consensus algorithms for multiagent networked systems with an emphasis on the role of directed information flow, robustness to changes in network topology due to link/node failures, timedelays, and performance guarantees. An overview
Wireless Communications
, 2005
"... Copyright c ○ 2005 by Cambridge University Press. This material is in copyright. Subject to statutory exception and to the provisions of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University ..."
Abstract

Cited by 1129 (32 self)
 Add to MetaCart
Copyright c ○ 2005 by Cambridge University Press. This material is in copyright. Subject to statutory exception and to the provisions of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University
Bundle Adjustment  A Modern Synthesis
 VISION ALGORITHMS: THEORY AND PRACTICE, LNCS
, 2000
"... This paper is a survey of the theory and methods of photogrammetric bundle adjustment, aimed at potential implementors in the computer vision community. Bundle adjustment is the problem of refining a visual reconstruction to produce jointly optimal structure and viewing parameter estimates. Topics c ..."
Abstract

Cited by 555 (12 self)
 Add to MetaCart
covered include: the choice of cost function and robustness; numerical optimization including sparse Newton methods, linearly convergent approximations, updating and recursive methods; gauge (datum) invariance; and quality control. The theory is developed for general robust cost functions rather than
Estimating the Support of a HighDimensional Distribution
, 1999
"... Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propo ..."
Abstract

Cited by 766 (29 self)
 Add to MetaCart
algorithm. The algorithm is a natural extension of the support vector algorithm to the case of unlabelled d...
Results 1  10
of
21,650